This study examines the impact of Islamic Work Ethic on organizational citizenship behaviors and knowledge-sharing behaviors among university employees in Pakistan. A total of 215 respondents from public sector educational institutions participated in this research. The findings suggest that IWE has a positive effect on OCBs. In other words, individuals with high IWE demonstrate more citizenship behaviors than those with low IWE. The findings also suggest a positive effect of IWE on KSBs. Individuals with high IWE exhibit more KSBs than (...) those with low IWE. The paper also discusses the theoretical and practical implications of these findings. (shrink)

Cardiovascular diseases, including heart disease, pose a significant global health challenge, contributing to a substantial burden on healthcare systems and individuals. Early detection and accurate prediction of heart disease are crucial for timely intervention and improved patient outcomes. This research explores the potential of neural networks in predicting heart disease using a dataset collected from Kaggle, consisting of 1025 samples with 14 distinct features. The study's primary objective is to develop an effective neural network model for binary classification, identifying the (...) presence or absence of heart disease. The neural network architecture includes an input layer, a hidden layer, and an output layer, designed to capture intricate relationships within the dataset. Rigorous training and validation processes, accompanied by data preprocessing steps, ensure the model's robustness and generalization capabilities. The results demonstrate promising performance, with an accuracy of 92% and an average error of 0.062. Moreover, an analysis of feature importance highlights key predictors, including "oldpeak," "thalach," "trestbps," "ca," "thal," "cp," "chol," "sex," "restecg," "age," "slope," "fbs," and "exang." This research contributes to the field of predictive healthcare by leveraging neural networks to enhance heart disease prediction. The developed model offers the potential for early identification of individuals at risk, facilitating timely medical interventions and ultimately improving public health. Further exploration of machine learning techniques in healthcare promises to reshape disease prediction and prevention strategies. (shrink)

This article explores the essence of the human (insān) as it is understood in Twelver Shīʿī philosophy and mysticism. It presents a Shīʿī philosophical elucidation regarding the possible existence of extraterrestrial intelligent lifeforms and what their relationship with “humanhood” might be. This line of reasoning is presented with a general sketch of how, in Shīʿī Islamic thought, a “human being” is characterized by specific traits and the relationship of human beings with the archetype of the Perfect Human (al‐Insān al‐Kāmil). Following (...) this is a review of Shīʿī Imāmī traditions regarding extraterrestrial intelligent life and the plurality of worlds. This sequence ultimately allows for a unique analysis of humanhood according to the Shīʿī philosophical viewpoint and helps determine if the term “human” can be used for other intelligent beings with similar ontological features and intelligence levels. (shrink)

As parents and students wished not to be closed these schools because the direct victim will be the students if any action form government is taken for shutting down the schools. These schools should be handed over to local management. About the issue of closing Pak-Turk Schools, Imran Khan, head of the leading political party, now the Prime Minister of Pakistan, said that Pakistan would respect the Turk government’s decision; however, he suggested an amicable solution of the issue so as (...) to protect the future of the students as well as of the teachers. According to the press statement released from PTI Central Media Department, during the meeting they discussed the issues of mutual importance Now the local administration should develop diversification in vision and mission of the organization. (shrink)

This study aims to identify social media and its relation with depression and how social media affects the mental health of individuals. The general Pakistani public who have attended college and are well educated is the study's target population. This research is based on a quantitative technique. A modified questionnaire was used in accordance with the study's objectives. The data was collected using Google forms. Five-point likert scales were preferred for the data collection when convenience sampling was used. The five-point (...) Likert scale served as the foundation for the survey. The ADANCO software was used to carry out the testing. These tests include the convergent validity, discriminant validity, and Cronbach's alpha reliability and validity tests. ADANCO has been used to measure the path coefficient, adjusted (R2), and coefficient of determination (R2). The confined areas for answers in Pakistan were the main focus of this investigation. The sample size for this study is small relative to the population because it was completed in a short time. The findings of this study show that social media can eventually lead to depression. In this study, the elements that affect mental health by excessive use of social media were examined. Numerous studies have been conducted on the detection of depression by the use of social media through bagging classifiers. We have collected data on the detection of depression through bagging classifiers and have added it to our literature review. (shrink)

Abstract: This research paper presents the development and evaluation of a neural network-based model for predicting fire alarms in smoke detection systems. Using a dataset from Kaggle containing 15 features and 3487 samples, we trained and validated a neural network with a three-layer architecture. The model achieved an accuracy of 100% and an average error of 0.0000003. Additionally, we identified the most influential features in predicting fire alarms.

The elderly face very challenging situations due to their mental and physical conditions. Like the other country in the world, Bangladesh Government has enacted laws to protect the elderly rights. However, the law does not seem to represent what the elderly actually needs. Therefore, 385 elderly people, aged between 60 and 90 years were surveyed to understand their expectations from family, society, and government. There were 57.1% men and 42.9% women. Most of the elderly (80%) were educated. Just over half (...) (53.5%) had ordinary mental and physical health, while a quarter (31.4%) had good status. More than half (53%) of the participants required 2000–5000 BDTK (Bangladeshi Taka) equivalent to $20-50 to cover monthly treatment expenditures. The majority (67.3%) felt government Old Age Allowance should be increased to BDT 5,000 ($50). Of 13.8% of individuals experienced harsh discourse from family because of the cost of therapy. However, 16.9% of people choose not to respond to this question. More than half (57.4%) of the respondents’ thought caregivers were insufficient. One-third felt that legislation should be changed to take care of parents (33.5%) and One-third felt that children should take care of more (33.5%). Some (3.6%) thought that children should act as they did in their childhood respectively. Almost half (44.2%) of respondents did not know that the elderly should receive a separate senior citizen card for preferential treatment, yet 51.9% thought this. Some (44.7%) felt the elderly should get priority in any queue and discounts in Bus/Uber/Rickshaw. Results also showed that expectation is more than the service provided. Further, a one-stop elderly care clinic is required to provide integrated care and support for the quality of life of the elderly. (shrink)

This tenth volume of Collected Papers includes 86 papers in English and Spanish languages comprising 972 pages, written between 2014-2022 by the author alone or in collaboration with the following 105 co-authors (alphabetically ordered) from 26 countries: Abu Sufian, Ali Hassan, Ali Safaa Sadiq, Anirudha Ghosh, Assia Bakali, Atiqe Ur Rahman, Laura Bogdan, Willem K.M. Brauers, Erick González Caballero, Fausto Cavallaro, Gavrilă Calefariu, T. Chalapathi, Victor Christianto, Mihaela Colhon, Sergiu Boris Cononovici, Mamoni Dhar, Irfan Deli, Rebeca Escobar-Jara, Alexandru Gal, N. (...) Gandotra, Sudipta Gayen, Vassilis C. Gerogiannis, Noel Batista Hernández, Hongnian Yu, Hongbo Wang, Mihaiela Iliescu, F. Nirmala Irudayam, Sripati Jha, Darjan Karabašević, T. Katican, Bakhtawar Ali Khan, Hina Khan, Volodymyr Krasnoholovets, R. Kiran Kumar, Manoranjan Kumar Singh, Ranjan Kumar, M. Lathamaheswari, Yasar Mahmood, Nivetha Martin, Adrian Mărgean, Octavian Melinte, Mingcong Deng, Marcel Migdalovici, Monika Moga, Sana Moin, Mohamed Abdel-Basset, Mohamed Elhoseny, Rehab Mohamed, Mohamed Talea, Kalyan Mondal, Muhammad Aslam, Muhammad Aslam Malik, Muhammad Ihsan, Muhammad Naveed Jafar, Muhammad Rayees Ahmad, Muhammad Saeed, Muhammad Saqlain, Muhammad Shabir, Mujahid Abbas, Mumtaz Ali, Radu I. Munteanu, Ghulam Murtaza, Munazza Naz, Tahsin Oner, ‪Gabrijela Popović‬‬‬‬‬, Surapati Pramanik, R. Priya, S.P. Priyadharshini, Midha Qayyum, Quang-Thinh Bui, Shazia Rana, Akbara Rezaei, Jesús Estupiñán Ricardo, Rıdvan Sahin, Saeeda Mirvakili, Said Broumi, A. A. Salama, Flavius Aurelian Sârbu, Ganeshsree Selvachandran, Javid Shabbir, Shio Gai Quek, Son Hoang Le, Florentin Smarandache, Dragiša Stanujkić, S. Sudha, Taha Yasin Ozturk, Zaigham Tahir, The Houw Iong, Ayse Topal, Alptekin Ulutaș, Maikel Yelandi Leyva Vázquez, Rizha Vitania, Luige Vlădăreanu, Victor Vlădăreanu, Ștefan Vlăduțescu, J. Vimala, Dan Valeriu Voinea, Adem Yolcu, Yongfei Feng, Abd El-Nasser H. Zaied, Edmundas Kazimieras Zavadskas.‬‬. (shrink)